DNNs have led to a series of breakthroughs for image classification [1]}, [2]}. To improve classification accuracy, researchers proposed different kinds of strategies, including designing better network structures (e.g., ResNet [3]} and DenseNet [4]}), designing better optimizers (e.g., Adam optimizer [5]} and RMSprop optimizer [6]}), applying better loss formulation (e.g., contrastive loss [7]}), and using large-scale pretraining [8]}.
<FIGURE>